Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

OBJECTIVE To systematically compare mature experiences of comprehensive drug value assessment in typical countries/regions and to provide decision-making references for China to establish a scientific and standardized comprehensive drug value assessment system for medical-insured drugs. METHODS The literature analysis was used to systematically review drug value assessment frameworks in 11 representative countries/regions， namely the UK， Canada， Italy， Australia， Germany， France， South Korea， Japan， the United States， as well as Taiwan （China） and Hong Kong （China）. Comparisons were made across three dimensions： assessment entities， value dimension， and application of results. RESULTS &CONCLUSIONS In most countries/regions， independent technical assessment institutions have been established as part of the drug value evaluation system， with the involvement of multiple stakeholders （e.g.， the UK， Canada）. The mainstream drug value assessment frameworks have generally transcended the traditional core dimensions of safety， efficacy， and cost-effectiveness， exhibiting two major trends： the continuous expansion of assessment dimensions and stricter evidence requirements. Assessment outcomes are closely integrated with payment policies， ranging from providing technical advice for decision-making （e.g.， Italy， France） to directly determining reimbursement eligibility （e.g.， the UK， Germany）. The following recommendations are proposed for China： first， establish an evaluation mechanism featuring multi-stakeholder participation and separation of evaluation from decision-making. Second， develop a comprehensive evaluation framework integrating clinical， economic， patient， and societal value， emphasizing quantitative indicator exploration and real-world evidence application. Third， promote direct linkage between value-based tiering outcomes and medical insurance reimbursement decisions or access negotiations to balance patient benefits， fund sustainability， and industrial innovation.

Objective:To investigate the current status of swallowing dysfunction in hospitalized very elderly patients and analyze the related influencing factors.Methods:A cross-sectional study was conducted, selecting data from 72 very elderly patients aged 85-100 years(mean age: 91.5±3.9 years)who met the inclusion criteria in the geriatrics department of a tertiary hospital in Guangzhou from July to December 2023.A comprehensive geriatric assessment was performed, including tools for orofacial function, nutrition, frailty, polypharmacy, comorbidity index, sarcopenia, cognition, and emotional/psychological status.Swallowing dysfunction was screened and its severity assessed using the EAT-10 and SSA scales, followed by analysis of related influencing factors.Results:Among the 72 very elderly patients, EAT-10 screening indicated a positive rate of swallowing dysfunction of 83.3%(60/72). Univariate analysis showed that age, body mass index, history of choking, nutritional status, cognitive function, frailty, comorbidity index, and calf circumference were associated with swallowing dysfunction, with statistically significant differences(all P<0.05). Multivariate logistic regression analysis revealed that age( OR=1.079, 95% CI: 1.011-1.151), nutritional status( OR=3.709, 95% CI: 1.825-7.540), impaired activities of daily living( OR=0.723, 95% CI: 0.578-0.905), frailty( OR=1.640, 95% CI: 1.274-2.110), and number of falls( OR=1.922, 95% CI: 1.050-2.984)were correlated with swallowing dysfunction(all P<0.05). Conclusions:The prevalence of swallowing dysfunction is high among hospitalized very elderly patients, with age, nutritional status, number of falls, activities of daily living, and frailty identified as independent risk factors.Early risk screening and intervention for swallowing function should be strengthened clinically to reduce complications associated with swallowing dysfunction.

Objective:To explore the application value of the double-low technique of auto-prescription technique combined with iterative reconstruction algorithm in CT pulmonary angiography (CTPA).Methods:A total of 86 patients who were clinically suspected of having pulmonary embolism and underwent CTPA examination in the First Affiliated Hospital of Dalian Medical University were prospectively collected and randomly assigned to a control group ( n = 45) and an observation group ( n = 41) according to the random number table method. In the control group, a tube voltage of 120 kVp was used with a standard iodine contrast agent dose of 60 ml, and images were reconstructed using the 40% adaptive statistical iterative reconstruction algorithm (ASIR-V). In the observation group, the tube voltage was set by auto-prescription technique, and 0.4 ml/kg of personalized low iodine contrast agent was used. Images were reconstructed with 40%, 60%, and 80% ASIR-V, respectively, and designated as observation 1, observation 2, and observation 3 respectively. The volume CT dose index (CTDI vol), dose-length product (DLP), and effective dose ( E) were recorded and compared among the four groups. The CT values and standard deviation (SD) of the main pulmonary artery, left and right pulmonary arteries, as well as the left and right pulmonary lobe arteries were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of these arteries were calculated. Additionally, the SD value at the contrast medium concentration in the superior vena cava was measured, and the artifact index (AI) was subsequently calculated. Two observers independently assessed the visibility of the pulmonary arteries, image noise, and sclerosis artifacts in the superior vena cava using a blinded method. Results:The E in the observation group was 3.28 (2.08, 3.93) mSv, which was significantly lower than that in the control group [5.03 (4.86, 5.20)] mSv, and the difference was statistically significant ( Z = 174.00, P < 0.05). The contrast agent dosage in the observation group was 28 (25, 30) ml, which was lower than that in the control group (60 ml), and the difference was statistically significant ( Z = 0, P < 0.05). The CT values for the main pulmonary artery and the left and right pulmonary lobe arteries in the observation group were higher than those in the control group, and the differences were all statistically significant ( t = -3.65 to -3.89, P < 0.05). The SNR and CNR of the observation groups 2 and 3 were greater than those of the control group ( t = -9.20 to -2.98, P < 0.05). The consistency of subjective evaluations between the two observers was good ( Kappa = 0.729 - 0.879, P < 0.05). There was no statistically significant difference in the subjective score of pulmonary artery visibility between the control and observation group ( P > 0.05). The subjective scores for image noise in observation group 2 and group 3 were higher than those in the control group ( U =598.50, 654.00, P < 0.05). The presence of artifacts due to sclerosis in the superior vena cava was significantly lower in the observation group compared to the control group ( χ2 = 46.09, P < 0.001). Conclusions:The combination of auto-prescription technique with ASIR-V reconstruction algorithm and low contrast agent imaging protocol can reduce the radiation dose and contrast agent dose without compromising image quality, and enable personalized double low CTPA imaging.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective To explore the feasibility of constructing a three-classification bone status screening radiomics model on chest CT images.Methods A total of 371 patients who underwent both chest and abdominal plain CT examinations were retrospec-tively selected and randomly divided into training set(296 cases)and test set(75 cases)in a ratio of 8︰2.Additionally,110 patients were included as external validation set using the same criteria.The 120 kVp abdominal images were transmitted to a quantitative compu-ted tomography(QCT)post-processing workstation to measure the bone mineral density(BMD)of the L1-L2 vertebral bodies.Patients were classified into osteoporosis(OP)group(BMD<80 mg/cm3),osteopenia group(80 mg/cm3≤BMD≤120 mg/cm3)and normal bone mass group(BMD>120 mg/cm3)based on QCT BMD results.The automatic segmentation model was used to segment T10-T12 vertebral trabecular bone on chest CT images and the radiomics models based on random forest(RF)and logistic regres-sion(LR)was established to evaluate BMD,enabling it to simultaneously distinguish OP,osteopenia,and normal bone mass.The diag-nostic performance of the two models were evaluated using metrics such as the area under the curve(AUC),sensitivity and specificity.The DeLong test was used to compare the differences between the two models.Results In the test set,the AUC for differentiating normal bone mass were 0.948 and 0.877 for the RF and LR models,respectively;the AUC for differentiating OP were 0.942 and 0.836,respectively;and the AUC for differentiating osteopenia were 0.871 and 0.688,respectively.The performance comparison results of the models showed that there was no statistically significant difference in AUC(0.966 vs 0.907,P>0.05)between RF model and LR model in the external validation set for distinguishing OP,while there was a statistically significant difference in AUC for distinguishing osteopenia(0.895 vs 0.749,P=0.009)and normal bone mass(0.975 vs 0.906,P=0.023).The RF model performance was superior to the LR model.Conclusion The radiomics model developed based on chest plain CT can be used for opportunistic OP screening with good diagnostic efficacy,and the the model based on the RF classifier outperforms the LR model.

Objective To explore the feasibility of spectral CT material decomposition technique Ca(Iodine)for opportunistic osteoporo-sis screening in enhanced scanning.Methods A total of 314 patients who underwent abdominal enhancement were selected.They were divided into group A and group B according to gender,and the groups were divided into three subgroups according to age(18-45 years,46-60 years,>60 years).The bone mineral density(BMD)values of L1-L3 vertebral were measured in the unenhanced images using quan-titative computed tomography(QCT)software,and Ca(Iodine)values were measured on three enhancement phases Ca(Iodine)based substance images.Pearson correlation analysis was performed between iodine intake and Ca(Iodine)values in three enhancement phases,Ca(Iodine)values and quantitative computed tomography bone mineral density(BMDQCT),and the diagnostic efficacy was analyzed.Results There was no significant difference in the Ca(Iodine)values of L1-L3 vertebral in three enhancement phases(F=0.001-0.018,P>0.05);there was no correlation between the Ca(Iodine)values of L1-L3 vertebral in three enhancement phases and iodine intake(r=0.073-0.105,P>0.05).Six groups of measured Ca(Iodine)values and BMDQCT both had a strong positive correlation(r=0.901-0.954,P<0.05).The Ca(Iodine)cutoff values for the diagnosis of osteoporosis and osteopenia were 830.41(2 mg/cm3)and 849.32(2 mg/cm3)respectively,with corresponding area under the curve(AUC)of 0.969[95%confidence interval(CI)0.943-0.985]and 0.973(95%CI 0.944-0.989),respectively.There were statistically significant differences in Ca(Iodine)values and BMDQCT in the>60 years age group between different genders(t=3.081-3.091,P<0.05).Conclusion Spectral CT material decomposition technique can be used for oppor-tunistic osteoporosis screening in enhanced scanning,with good diagnostic performance,which provides a new perspective for the clin-ical diagnosis of osteoporosis.

Objective:To explore the stratified treatment plan process for neonatal congenital chylothorax by summarizing its clinical treatment characteristics.Methods:A retrospective analysis was conducted on the clinical data of 36 neonates with congenital chylothorax treated at the Department of Neonatology of Beijing Children's Hospital, Capital Medical University, from January 1, 2010, to December 31, 2021. Based on different treatment methods and initial drainage volumes, the cases were divided into the conservative treatment group [initial drainage volume<20 ml/(kg·d), n=20], octreotide group [initial drainage volume ≥20-<30 ml/(kg·d), n=4], erythromycin group [initial drainage volume ≥30-<50 ml/(kg·d), n=6], and octreotide plus erythromycin group [initial drainage volume≥50 ml/(kg·d), n=6]. The clinical characteristics and treatment effects of the children in different treatment groups, as well as the choice of further treatment plans, were summarized to determine the timing of different treatment methods. A more standardized stratified treatment plan was formulated by combining the literature. Results:Among the 36 cases of congenital chylothorax, 18 cases (50.0%) were diagnosed in utero, with no intrauterine intervention. In the conservative treatment group, 20 cases were treated with respiratory support, thoracic drainage, and nutritional therapy. Except for one case who was discharged after abandoning treatment, the remaining 19 cases were cured. In the octreotide group, four children received continuous intravenous infusion of octreotide at doses ranging from 1 to 10 μg/(kg·h), with three cases improving and one case being cured. No adverse effects such as hypoglycemia, thyroid dysfunction, or neonatal necrotizing enterocolitis occurred. In the group of six children who received intrapleural erythromycin injections, the dosage of erythromycin was 25-30 mg/kg, and the median thoracic drainage volume was reduced to approximately 50% of the pre-treatment volume after 3-5 injections, with four cases improving and two cases being cured. In the group of six children who received octreotide combined with erythromycin, treatment involved the intravenous infusion of octreotide along with intrapleural erythromycin injections. Four cases showed improvement, and two cases were cured. Based on previous treatments and a comprehensive review of the literature, a stratified treatment flowchart with invasiveness ranging from low to high was finally formed. Conclusions:For congenital chylothorax, a stratified treatment approach is recommended based on initial drainage volume and the response to treatment. This approach ranges from conservative treatment to pharmacological treatment (intravenous infusion of octreotide). For children with poor outcomes, surgical treatment (intrapleural erythromycin injection or other surgical interventions) can be added.

The incidence of less common tumors is intermediate between rare tumors and high-prevalence tumors,while these less common tumors such as biliary tract cancers generate a significant regional health burden. The overall incidence of less common tumors is relatively low, and thus their clinical epidemiological studies face challenges such as recruitment difficulties,poor representation,and low standardization. Surgical center-based disease-specific cohorts have the advantages of case concentration,complete samples,and well-developed data,which are uniquely valuable in clinical epidemiological studies. Taking the disease-specific cohort of biliary tract cancers as an example,the authors combed through the relevant references and summarized the thinking in the practice of constructing disease-specific cohorts of less common tumors based on surgical centers. The architecture of the disease-specific cohort construction has been generalized as follows: the hardware includes a database, a biobank, and a platform of information synchronization, and the software follows the design principle of “high cohesion and low coupling”. The authors also recommend an orderly expansion of study size and implementation of quality control through all segments of cohort construction, and hope that these reflections could provide a reference for similar disease-specific cohorts.

Aim To explore the predictive value of stress hyperglycemia ratio(SHR)for in-hospital mortality and mechanical complications in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods This study constituted a retrospective investigation that collected 995 patients diagnosed with acute STEMI at Suining Central Hospital from June 2019 to July 2023.Comparisons of baseline data were conducted using t-test,Mann-Whitney U test and chi-square test;Logistic regression was used to analyze the association between SHR and the risk of in-hospital mortality and mechanical complications in acute STEMI patients;Restricted cubic spline analysis based on the Logistic re-gression model was utilized to explore non-linear relationship between SHR and the risk of in-hospital mortality and mechan-ical complications;ROC curve was used to evaluate the diagnostic efficacy of SHR;Subgroup analysis was used to assess the predictive efficacy of SHR in each subgroup.Results Patients with high SHR had a significantly higher cardiovas-cular mortality(P=0.007).High SHR was an independent risk factor for in-hospital all-cause mortality(Model 1:OR=3.085,95％ CI:1.719～5.538,P＜0.001;Model 2:OR=2.738,95％ CI:1.4439～5.132,P=0.002),cardiovascular mortality(Model 1:OR=3.406,95％ CI:1.869～6.228,P＜0.001;Model 2:OR=3.053,95％ CI:1.595～5.817,P＜0.001),ventricular aneurysm(Model 1:OR=3.203,95％CI:1.665～6.069,P＜0.001;Model2:OR=3.93,95％CI:1.785～8.663,P＜0.001),cardiac rupture(Model 1:OR=2.461,95％ CI:1.389～4.312,P=0.002;Model 2:OR=2.302,95％ CI:1.214～4.274,P=0.009)and composite endpoint(Model 1:OR=3.719,95％ CI:2.226～6.332,P＜0.001;Model 2:OR=2.919,95％ CI:1.576～5.405,P＜0.001)in patients with acute STEMI.SHR was positively correlated in a linear relationship with the risk of in-hospital all-cause mortality(P for non-linearity=0.250),cardiovascular mortality(P for non-linearity=0.129),ventricular aneurysm(P for non-linearity=0.588),cardiac rupture(P for non-linearity=0.787)and composite endpoint(P for non-linearity=0.399).The SHR had excellent diagnostic efficacy for in-hospital all-cause mortality(AUC=0.694),cardiovascular mortality(AUC=0.697),ventricular aneurysm(AUC=0.706),cardiac rupture(AUC=0.667)and composite endpoint(AUC=0.730),meanwhile SHR predicted efficacy consistently across subgroups.Conclusions High SHR is an independent risk factor for in-hospital all-cause mortality,cardiovascular mortality and cardiac mechanical complications in patients with a-cute STEMI.SHR holds significant predictive value for the prognosis of patients with STEMI.